Sujit Singh

The chapter addresses the dis-ruptive power of AI-based cognitive robotics to revamp autonomous multi-material sorting and high-value recovery, in the case of the e-waste industry. Based on the analysis of cur-rent academic literature (2019-2025), this paper will generalize the emergence of artificial intelligence (AI), machine learning (ML), deep learning (DL), computer vision (CV), rein-forcement learning (RL), and enhanced robotic manipulation to outline an innovative con-cept of intelligent waste management. The methodologies include the wide range of robotic architecture, advanced AI routines to recognize and classify materials, and improved sorting routines that can classify complex waste streams more precisely and efficiently than ever before. In the chapter, a conceptual design of an AI-based cognitive robotic system that in-corporates sensory gain, cognitive automation, increased physical abilities, sensory-motor convergence, and human-machine cooperation to develop the highest value of material re-covery is suggested.

Conflict zones are at the crossroads of efficiency, humanitarian need, and geopolitical power, in which politics of access and chronic uncertainty reform flows in logistics and supply chains. Prolonged wars in Yemen, Syria, Libya, and Gaza have destroyed infrastructure, disintegrated power, and made logistics an instrument of power disputed in MENA. Chokepoint disruption at Bab el-Mandeb and the Strait of Hormuz disrupts routes, increases costs and emissions, compromising resilience on the global scale This chapter maps six categories of vulnerabilities and makes the case using the SCOR model that conflict-sensitive adaptation and practical corridor, partnership, sanctions, and digital-visibility actions are needed.

The objective of this chapter is to propose a modernized framework for IT project management that addresses the shortcomings of traditional methodologies. It will argue for a shift from rigid, reactive models to an adaptive, proactive approach. The chapter demonstrates how integrating real-time data analytics and continuous risk monitoring can enhance flexibility, improve decision-making, and prevent the accumulation of unseen risks. Ultimately, it aims to outline the principles of a data-driven management strategy capable of delivering successful project outcomes in today's volatile technological landscape.

Purpose: This study aims to determine and prioritize the critical success factors (CSFs) that drive the procurement process of artificial intelligence (AI) systems in the public sector in the Indian context. It addresses the peculiar challenges and exigencies of procurement processes of AI systems in public sector organizations and develops a hierarchical model for guiding policymakers. Design/methodology/approach: This study uses a research methodology that includes: identifying potential CSFs through literature review and expert consultations, developing a structural self-interaction matrix and performing cross-impact matrix multiplication applied to classification analysis for classifying CSFs based on driving and dependence powers. The study also uses interpretive structural modelling to establish relationships among these factors. Findings: Fourteen CSFs are found in this study, which shape public procurement of AI. These influential factors cut across technical, organizational, regulatory as well as socio-economic aspects. This model is arranged hierarchically to indicate how these identified CSFs fit together thereby providing a roadmap towards successful procurement of AI systems. All other CSFs depend on or are influenced by stakeholders’ coordination and collaboration, which means that it is the most important one. Practical implications: The results present a hierarchical model for navigating the intricacies of public sector AI procurement. Consequently, this model may support public organizations to enhance service delivery, optimize resource allocation and improve decision-making processes. It also addresses the need for an organized approach to the procurement of AI systems in developing countries such as India where extant guidelines are either insufficient or non-existent. Implementing these CSFs can result in more efficient, transparent and accountable procurement practices of AI systems, ultimately impacting positively on public service outcomes. Social implications: The hierarchical model and classification of CSFs provide an alternative view regarding what entails successful AI systems’ acquisition stressing out particular requirements and obstacles associated with it within developing countries’ public sector agencies. It is in line with achieving sustainable development goals (SDGs) 9 which is industry, innovation and infrastructure and SDG 12 which is responsible consumption and production. Originality/value: This study provides a new perspective by developing a comprehensive structured framework for the procurement of AI systems in the public sector. It fills gaps existing in research by offering a structural model on how to increase organization efficiency through the procurement of AI systems. The hierarchical model and classification of CSFs provide an alternative view regarding what entails successful AI acquisition, stressing particular requirements and obstacles associated with it within developing countries’ public sector agencies.

This study explores the barriers that Indian Small and Medium Enterprises (SMEs) encounter when implementing Industry 4.0 technologies. Industry 4.0, marked by cyber-physical systems, data analytics, and automation, offers significant potential for enhancing efficiency and productivity. Despite this, Indian SMEs face substantial challenges in implementing these technologies. The research utilizes a combined Multi-Criteria Decision Making (MCDM) approach, integrating the Analytic Hierarchy Process (AHP) and VIKOR methods. AHP helps prioritize various barriers based on expert opinions and their relative importance. After identifying and weighing the barriers, VIKOR is used to rank them. VIKOR evaluates the “closeness” to the ideal solution and the “maximum group utility distance” from other options. This integrated approach offers a comprehensive way to identify the most critical barriers and develop strategies to address them. The results of this study can aid in designing targeted interventions to overcome these challenges. By understanding the specific issues Indian SMEs face, policymakers, industry leaders, and technology providers can create a more favorable environment for adopting Industry 4.0. This, in turn, can help Indian SMEs harness the transformative power of this new industrial revolution.